
import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer







def 		_snake_case(    SCREAMING_SNAKE_CASE__							)					->       List[Any]:
       # This defines a "chinese character" as anything in the CJK Unicode block:
       #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
       #
       # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
       # despite its name. The modern Korean Hangul alphabet is a different block,
       # as is Japanese Hiragana and Katakana. Those alphabets are used to write
       # space-separated words, so they are not treated specially and handled
       # like the all of the other languages.
       if (
           (cp >= 0x4_e_0_0 and cp <= 0x9_f_f_f)
           or (cp >= 0x3_4_0_0 and cp <= 0x4_d_b_f)  #
           or (cp >= 0x2_0_0_0_0 and cp <= 0x2_a_6_d_f)  #
           or (cp >= 0x2_a_7_0_0 and cp <= 0x2_b_7_3_f)  #
           or (cp >= 0x2_b_7_4_0 and cp <= 0x2_b_8_1_f)  #
           or (cp >= 0x2_b_8_2_0 and cp <= 0x2_c_e_a_f)  #
           or (cp >= 0xf_9_0_0 and cp <= 0xf_a_f_f)
           or (cp >= 0x2_f_8_0_0 and cp <= 0x2_f_a_1_f)  #
       ):  #
              return True

       return False







def 		_snake_case(    SCREAMING_SNAKE_CASE__							)					->       Optional[Any]:
       # word like '180' or '身高' or '神'
       for char in word:
              lowercase				:	str			      = ord(SCREAMING_SNAKE_CASE__							)
              if not _is_chinese_char(SCREAMING_SNAKE_CASE__							):
                     return 0
       return 1







def 		_snake_case(    SCREAMING_SNAKE_CASE__							)					->       Dict:
       lowercase				:	Optional[Any]			      = set()

       for token in tokens:
              lowercase				:	str			      = len(SCREAMING_SNAKE_CASE__							) > 1 and is_chinese(SCREAMING_SNAKE_CASE__							)
              if chinese_word:
                     word_set.add(SCREAMING_SNAKE_CASE__							)
       lowercase				:	Optional[int]			      = list(SCREAMING_SNAKE_CASE__							)
       return word_list







def 		_snake_case(    SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							)					->       Optional[Any]:
       if not chinese_word_set:
              return bert_tokens
       lowercase				:	Dict			      = max([len(SCREAMING_SNAKE_CASE__							) for w in chinese_word_set]							)

       lowercase				:	List[str]			      = bert_tokens
       lowercase				:	List[Any]			      = 0, len(SCREAMING_SNAKE_CASE__							)
       while start < end:
              lowercase				:	str			      = True
              if is_chinese(bert_word[start]							):
                     lowercase				:	str			      = min(end - start   ,      SCREAMING_SNAKE_CASE__							)
                     for i in range(SCREAMING_SNAKE_CASE__   ,      1   ,      -1							):
                            lowercase				:	Tuple			      = ''''''.join(bert_word[start : start + i]							)
                            if whole_word in chinese_word_set:
                                   for j in range(start + 1   ,      start + i							):
                                          lowercase				:	Union[str, Any]			      = '''##''' + bert_word[j]
                                   lowercase				:	Any			      = start + i
                                   lowercase				:	Optional[int]			      = False
                                   break
              if single_word:
                     start += 1
       return bert_word







def 		_snake_case(    SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							)					->       Union[str, Any]:
       lowercase				:	Optional[int]			      = []

       for i in range(0   ,      len(SCREAMING_SNAKE_CASE__							)   ,      100							):
              lowercase				:	int			      = ltp_tokenizer.pipeline(lines[i : i + 100]   ,      tasks=["""cws"""]							).cws
              lowercase				:	Any			      = [get_chinese_word(SCREAMING_SNAKE_CASE__							) for r in res]
              ltp_res.extend(SCREAMING_SNAKE_CASE__							)
       assert len(SCREAMING_SNAKE_CASE__							) == len(SCREAMING_SNAKE_CASE__							)

       lowercase				:	Tuple			      = []
       for i in range(0   ,      len(SCREAMING_SNAKE_CASE__							)   ,      100							):
              lowercase				:	Optional[int]			      = bert_tokenizer(lines[i : i + 100]   ,      add_special_tokens=SCREAMING_SNAKE_CASE__   ,      truncation=SCREAMING_SNAKE_CASE__   ,      max_length=512							)
              bert_res.extend(res["""input_ids"""]							)
       assert len(SCREAMING_SNAKE_CASE__							) == len(SCREAMING_SNAKE_CASE__							)

       lowercase				:	Any			      = []
       for input_ids, chinese_word in zip(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							):
              lowercase				:	Union[str, Any]			      = []
              for id in input_ids:
                     lowercase				:	Union[str, Any]			      = bert_tokenizer._convert_id_to_token(SCREAMING_SNAKE_CASE__							)
                     input_tokens.append(SCREAMING_SNAKE_CASE__							)
              lowercase				:	List[str]			      = add_sub_symbol(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							)
              lowercase				:	Any			      = []
              # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
              for i, token in enumerate(SCREAMING_SNAKE_CASE__							):
                     if token[:2] == "##":
                            lowercase				:	int			      = token[2:]
                            # save chinese tokens' pos
                            if len(SCREAMING_SNAKE_CASE__							) == 1 and _is_chinese_char(ord(SCREAMING_SNAKE_CASE__							)							):
                                   ref_id.append(SCREAMING_SNAKE_CASE__							)
              ref_ids.append(SCREAMING_SNAKE_CASE__							)

       assert len(SCREAMING_SNAKE_CASE__							) == len(SCREAMING_SNAKE_CASE__							)

       return ref_ids







def 		_snake_case(    SCREAMING_SNAKE_CASE__							)					->       Tuple:
       # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
       # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
       with open(args.file_name   ,      """r"""   ,      encoding="""utf-8"""							) as f:
              lowercase				:	Optional[Any]			      = f.readlines()
       lowercase				:	List[str]			      = [line.strip() for line in data if len(SCREAMING_SNAKE_CASE__							) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
       lowercase				:	Tuple			      = LTP(args.ltp							)  # faster in GPU device
       lowercase				:	Union[str, Any]			      = BertTokenizer.from_pretrained(args.bert							)

       lowercase				:	Optional[int]			      = prepare_ref(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							)

       with open(args.save_path   ,      """w"""   ,      encoding="""utf-8"""							) as f:
              lowercase				:	str			      = [json.dumps(SCREAMING_SNAKE_CASE__							) + '''\n''' for ref in ref_ids]
              f.writelines(SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
  lowercase    :   Optional[Any]		       =				argparse.ArgumentParser(description="""prepare_chinese_ref""")
  parser.add_argument(
      """--file_name""",
      required=False,
      type=str,
      default="""./resources/chinese-demo.txt""",
      help="""file need process, same as training data in lm""",
  )
  parser.add_argument(
      """--ltp""",
      required=False,
      type=str,
      default="""./resources/ltp""",
      help="""resources for LTP tokenizer, usually a path""",
  )
  parser.add_argument(
      """--bert""",
      required=False,
      type=str,
      default="""./resources/robert""",
      help="""resources for Bert tokenizer""",
  )
  parser.add_argument(
      """--save_path""",
      required=False,
      type=str,
      default="""./resources/ref.txt""",
      help="""path to save res""",
  )

  lowercase    :   Any		       =				parser.parse_args()
  main(args)








'''simple docstring'''





import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase						:						Optional[Any]  =					logging.get_logger(__name__)

_UpperCamelCase						:						Dict  =					{
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}


class  snake_case__      (					UpperCamelCase):
					a_        =					"mvp"
					a_        =					["past_key_values"]
					a_        =					{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




					def __init__(  self  :		Any				, _A  :		List[str]=5_02_67				, _A  :		Dict=10_24				, _A  :		List[str]=12				, _A  :		Dict=40_96				, _A  :		Optional[int]=16				, _A  :		Optional[Any]=12				, _A  :		int=40_96				, _A  :		Dict=16				, _A  :		Union[str, Any]=0.0				, _A  :		Optional[Any]=0.0				, _A  :		int="gelu"				, _A  :		Union[str, Any]=10_24				, _A  :		int=0.1				, _A  :		Optional[Any]=0.0				, _A  :		Optional[Any]=0.0				, _A  :		Optional[Any]=0.02				, _A  :		Optional[Any]=0.0				, _A  :		str=False				, _A  :		List[Any]=True				, _A  :		Optional[int]=1				, _A  :		Tuple=0				, _A  :		Dict=2				, _A  :		Dict=True				, _A  :		int=2				, _A  :		Tuple=2				, _A  :		List[str]=False				, _A  :		Any=1_00				, _A  :		Union[str, Any]=8_00				, **_A  :		List[str]				, )    ->							int:
								UpperCAmelCase_      :       Union[str, Any]          =       vocab_size
								UpperCAmelCase_      :       int          =       max_position_embeddings
								UpperCAmelCase_      :       Dict          =       d_model
								UpperCAmelCase_      :       Dict          =       encoder_ffn_dim
								UpperCAmelCase_      :       Optional[Any]          =       encoder_layers
								UpperCAmelCase_      :       Tuple          =       encoder_attention_heads
								UpperCAmelCase_      :       List[Any]          =       decoder_ffn_dim
								UpperCAmelCase_      :       List[Any]          =       decoder_layers
								UpperCAmelCase_      :       Dict          =       decoder_attention_heads
								UpperCAmelCase_      :       str          =       dropout
								UpperCAmelCase_      :       Optional[Any]          =       attention_dropout
								UpperCAmelCase_      :       Optional[Any]          =       activation_dropout
								UpperCAmelCase_      :       Union[str, Any]          =       activation_function
								UpperCAmelCase_      :       Union[str, Any]          =       init_std
								UpperCAmelCase_      :       Optional[int]          =       encoder_layerdrop
								UpperCAmelCase_      :       Optional[int]          =       decoder_layerdrop
								UpperCAmelCase_      :       Dict          =       classifier_dropout
								UpperCAmelCase_      :       List[Any]          =       use_cache
								UpperCAmelCase_      :       Dict          =       encoder_layers
								UpperCAmelCase_      :       Tuple          =       scale_embedding  # scale factor will be sqrt(d_model) if True
								UpperCAmelCase_      :       int          =       use_prompt
								UpperCAmelCase_      :       Tuple          =       prompt_length
								UpperCAmelCase_      :       str          =       prompt_mid_dim

								super().__init__(
								    pad_token_id=_A				, bos_token_id=_A				, eos_token_id=_A				, is_encoder_decoder=_A				, decoder_start_token_id=_A				, forced_eos_token_id=_A				, **_A				, )

								if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''				, _A      ):
											UpperCAmelCase_      :       Optional[Any]          =       self.bos_token_id
											warnings.warn(
											    F"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
											    '''The config can simply be saved and uploaded again to be fixed.'''      )


